Misinformation threatens modern society by promoting distrust in science, changing
narratives in public health, heightening social polarization, and disrupting democratic …

This paper presents\textttConv-KNRM, a Convolutional Kernel-based Neural Ranking Model
that models n-gram soft matches for ad-hoc search. Instead of exact matching query and …

Term frequency is a common method for identifying the importance of a term in a query or
document. But it is a weak signal, especially when the frequency distribution is flat, such as …

Recent advances in automatic entity linking and knowledge base construction have resulted
in entity annotations for document and query collections. For example, annotations of …

Relevance is the most important factor to assure users' satisfaction in search and the
success of a search engine heavily depends on its performance on relevance. It has been …

The past decade has witnessed the emergence of several publicly available and proprietary
knowledge graphs (KGs). The depth and breadth of content in these KGs made them not …

Recently, a novel generative retrieval (GR) paradigm has been proposed, where a single
sequence-to-sequence model is learned to directly generate a list of relevant document …

We address the ad hoc document retrieval task by devising novel types of entity-based
language models. The models utilize information about single terms in the query and …

This paper presents EsdRank, a new technique for improving ranking using external semi-
structured data such as controlled vocabularies and knowledge bases. EsdRank treats …

Recently, the focus of many novel search applications shifted from short keyword queries to
verbose natural language queries. Examples include question answering systems and …